Renal Function During an Open-Label Prospective Observational Trial of Sitagliptin in Patients With Diabetes: A Sub-Analysis of the JAMP Study
Bibliographic record
Abstract
Background: The aim of the study was to determine the effects of sitagliptin on renal function in a diabetic population including patients with normal renal function. Methods: We analyzed the association between 12-month, 50 mg/day sitagliptin and renal function in outpatients with type 2 diabetes mellitus and poor blood glucose control in a subset of patients in the larger Januvia Multicenter Prospective Trial in Type 2 Diabetes observational study. Stratified analyses of changes in estimated glomerular filtration rate (eGFR) and urinary albumin-to-creatinine ratio (UACR) were performed. Factors associated with changes in eGFR at 3 months were examined by multivariate regression analysis. Results: Of the 779 patients enrolled, 585 were followed up for 12 months. eGFR decreased significantly from baseline at 3 and 12 months in patients with a baseline eGFR of ? 90 mL/min/1.73 m 2 and in those with a baseline eGFR of ? 60 to < 90 mL/min/1.73 m 2 . Conversely, eGFR tended to increase at 3 and 12 months in patients with a baseline eGFR of ? 45 to < 60 mL/min/1.73 m 2 and in those with a baseline eGFR of ? 30 to < 45 mL/min/1.73 m 2 . UACR decreased significantly (-21.6 (-46.8, 7.8)) at 3 months in patients with a baseline UACR of ? 30 mg/g Cre. Multivariate regression analysis of factors associated with changes in eGFR at 3 months revealed that higher baseline eGFR and greater decline in UACR were associated with more conspicuous decreases in eGFR. Conclusions: In this group of diabetic patients receiving sitagliptin, eGFR declined in patients with high baseline eGFR, but not in those with a low baseline eGFR. J Clin Med Res. 2018;10(1):32-40 doi: https://doi.org/10.14740/jocmr3225w
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".